Predicting postpartum female sexual interest/arousal disorder via adiponectin and biopsychosocial factors: a cohort-based decision tree study
Abstract After childbirth, women experience significant psychological, physiological, and hormonal changes. To better diagnose individuals at risk of postpartum complications, predictive models utilizing data mining and machine learning techniques can be instrumental. The C4.5 decision tree algorith...
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Nature Portfolio
2025-07-01
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| Series: | Scientific Reports |
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| Online Access: | https://doi.org/10.1038/s41598-025-12025-3 |
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| author | Saiedeh Sadat Hajimirzaie Najmeh Tehranian Amin Golabpour Ahmad Khosravi Seyed Abbas Mousavi Afsaneh Keramat Mehdi Mirzaii |
| author_facet | Saiedeh Sadat Hajimirzaie Najmeh Tehranian Amin Golabpour Ahmad Khosravi Seyed Abbas Mousavi Afsaneh Keramat Mehdi Mirzaii |
| author_sort | Saiedeh Sadat Hajimirzaie |
| collection | DOAJ |
| description | Abstract After childbirth, women experience significant psychological, physiological, and hormonal changes. To better diagnose individuals at risk of postpartum complications, predictive models utilizing data mining and machine learning techniques can be instrumental. The C4.5 decision tree algorithm effectively analyzes multiple variables to identify key relationships. The objective of the study was to predict Female Sexual Interest/Arousal Disorder (FSIAD) six months postpartum using serum adiponectin levels and biopsychosocial factors through decision tree analysis. A longitudinal cohort study was conducted with data from 170 pregnant women, collecting data at three points: the third trimester, 40 days postpartum, and six months postpartum. Blood samples were analyzed for adiponectin, estradiol, and testosterone. At the same time, participants completed assessments using the Female Sexual Function Index (FSFI), the World Health Organization Well-Being Index, a socioeconomic index, and a questionnaire on non-biological factors affecting sexual desire. The prevalence of FSIAD was found to be 29.7%, and the model achieved 93.7% accuracy in predicting FSIAD. Significant predictors included serum adiponectin (T1), estrogen (T3), waist circumference (T2, T3), orgasm disorder, and pain disorder, all with p-values < 0.05. The model provides a clinically valuable tool for early identification of at-risk women, allowing for timely intervention and personalized postpartum care. |
| format | Article |
| id | doaj-art-5059c530d42e400fb491faafaa39244b |
| institution | Kabale University |
| issn | 2045-2322 |
| language | English |
| publishDate | 2025-07-01 |
| publisher | Nature Portfolio |
| record_format | Article |
| series | Scientific Reports |
| spelling | doaj-art-5059c530d42e400fb491faafaa39244b2025-08-20T03:42:45ZengNature PortfolioScientific Reports2045-23222025-07-0115111010.1038/s41598-025-12025-3Predicting postpartum female sexual interest/arousal disorder via adiponectin and biopsychosocial factors: a cohort-based decision tree studySaiedeh Sadat Hajimirzaie0Najmeh Tehranian1Amin Golabpour2Ahmad Khosravi3Seyed Abbas Mousavi4Afsaneh Keramat5Mehdi Mirzaii6School of Nursing and Midwifery, Shahroud University of Medical SciencesDepartment of Reproductive Health and Midwifery, Faculty of Medical Sciences, Tarbiat Modares UniversitySchool of Allied Medical Sciences, Shahroud University of Medical SciencesOphthalmic Epidemiology Research Center, Department of Epidemiology, Shahroud University of Medical SciencesDepartment of Psychiatry, Psychiatry and Behavioral Sciences Research Center, Addiction Institute, Mazandaran University of Medical SciencesCenter for Health Related Social and Behavioral Sciences Research, Shahroud University of Medical SciencesSchool of Medicine, Shahroud University of Medical SciencesAbstract After childbirth, women experience significant psychological, physiological, and hormonal changes. To better diagnose individuals at risk of postpartum complications, predictive models utilizing data mining and machine learning techniques can be instrumental. The C4.5 decision tree algorithm effectively analyzes multiple variables to identify key relationships. The objective of the study was to predict Female Sexual Interest/Arousal Disorder (FSIAD) six months postpartum using serum adiponectin levels and biopsychosocial factors through decision tree analysis. A longitudinal cohort study was conducted with data from 170 pregnant women, collecting data at three points: the third trimester, 40 days postpartum, and six months postpartum. Blood samples were analyzed for adiponectin, estradiol, and testosterone. At the same time, participants completed assessments using the Female Sexual Function Index (FSFI), the World Health Organization Well-Being Index, a socioeconomic index, and a questionnaire on non-biological factors affecting sexual desire. The prevalence of FSIAD was found to be 29.7%, and the model achieved 93.7% accuracy in predicting FSIAD. Significant predictors included serum adiponectin (T1), estrogen (T3), waist circumference (T2, T3), orgasm disorder, and pain disorder, all with p-values < 0.05. The model provides a clinically valuable tool for early identification of at-risk women, allowing for timely intervention and personalized postpartum care.https://doi.org/10.1038/s41598-025-12025-3Sexual dysfunctionsPregnant womenPostpartum periodAdiponectinSocioeconomic factorsC4.5 decision tree algorithm |
| spellingShingle | Saiedeh Sadat Hajimirzaie Najmeh Tehranian Amin Golabpour Ahmad Khosravi Seyed Abbas Mousavi Afsaneh Keramat Mehdi Mirzaii Predicting postpartum female sexual interest/arousal disorder via adiponectin and biopsychosocial factors: a cohort-based decision tree study Scientific Reports Sexual dysfunctions Pregnant women Postpartum period Adiponectin Socioeconomic factors C4.5 decision tree algorithm |
| title | Predicting postpartum female sexual interest/arousal disorder via adiponectin and biopsychosocial factors: a cohort-based decision tree study |
| title_full | Predicting postpartum female sexual interest/arousal disorder via adiponectin and biopsychosocial factors: a cohort-based decision tree study |
| title_fullStr | Predicting postpartum female sexual interest/arousal disorder via adiponectin and biopsychosocial factors: a cohort-based decision tree study |
| title_full_unstemmed | Predicting postpartum female sexual interest/arousal disorder via adiponectin and biopsychosocial factors: a cohort-based decision tree study |
| title_short | Predicting postpartum female sexual interest/arousal disorder via adiponectin and biopsychosocial factors: a cohort-based decision tree study |
| title_sort | predicting postpartum female sexual interest arousal disorder via adiponectin and biopsychosocial factors a cohort based decision tree study |
| topic | Sexual dysfunctions Pregnant women Postpartum period Adiponectin Socioeconomic factors C4.5 decision tree algorithm |
| url | https://doi.org/10.1038/s41598-025-12025-3 |
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